GRIB Model

NBM Hawaii (National Blend of Models)

Provider:National Weather Service, NOAA (USA)
Model scope:Hawaii
Update frequency:every hour
Resolution:1.3nm, 2.5km
Model duration:53 forecasts starting at 0 hr, ending at 11 days
Parameters:wind, wind gust, rain, ensemble standard deviation
GRIB model date:Tue Jun 18 02:00:00 2024 UTC
Download date:Tue Jun 18 03:06:39 2024 UTC
Download delay:1hr 06min

Note: the Download delay is the amount of time required for the GRIB model to compute its forecast and then for the LuckGrib cluster to download the data and make it available. The LuckGrib delay is generally less than 10 minutes, the remainder of the delay is the model compute time.

About the National Blend of Models

The National Blend of Models (NBM) is an interesting suite of models, well worth considering as you evaluate weather systems.

The following is from an online description of the NBM:

The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. This new way to produce NDFD grids will be helpful providing forecasters with a suite of information to use for their forecasts. The NBM is considered an important part of the efforts to evolve NWS capabilities to achieve a Weather-Ready Nation.

As of Oct/2020, the NBM model is at version 4.0.

The quoted text above mentiones that both NWS and non-NWS models are blended into the NBM.

The list of model inputs, for version 3.0 of NBM, is:

The update to NBM, version 3.1, has added the following model inputs:

  • ECMWFD (European Centre for Medium-Range Weather Forecasts, deterministic - 0.25 degree)(CONUS, OCONUS) (0000, 1200 UTC runs)
  • ECMWFE (European Centre for Medium-Range Weather Forecasts, Ensemble - 1.0 degree)(CONUS, OCONUS) (0000, 1200 UTC runs)
  • NAVGEMD (Navy FNMOC Global deterministic - 0.50 degree For most elements, 1.0 degree for significant wave heights)(CONUS, OCONUS, and Oceanic domain) (0000, 0600, 1200, 1800 UTC runs)
  • GDPS (CMCD Environment Canada Global deterministic model (25km) for weather elements other than PoP12/QPF06)
  • RDPS (Canadian Regional deterministic model - 10km) (CONUS and Alaska) (0000, 0600, 1200, 1800 UTC runs)
  • REPS (Canadian Regional ensemble model - 15km) (CONUS) (0000 and 1200 UTC runs) (Precipitation products only)
  • HRRR Alaska (High Resolution Rapid Refresh - 3km) (a). 1-18 hours (0300, 0900, 1500, 2100 UTC runs) (b). 1-36 hours (0000, 0600, 1200, 1800 UTC runs)
  • HiResWindow ARW Mem2 (3-km High-Resolution Window Forecast System (configured like NSSL WRF) (CONUS and OCONUS)
  • WW3 (0.5 degree WaveWatchIII global deterministic model)
  • WW3 (0.5 degree WaveWatchIII global ensemble model)
  • WW3 (0.16 and 0.06 degree WaveWatchIII high resolution regional models) (CONUS and OCONUS)
  • GLW (2.5km Great Lakes Wave model)

The v3.2 update to NBM has added the following model inputs:

  • ACCESS-G (The Australian global model)
  • GFS-FV3 visibility
  • additional improvements for other weather elements (temperature, rain, …)

The v4.0 update to NBM has added the following model inputs:

  • GFS-MOS (0000-, 0600-, 1200-, and 1800-UTC cycles)
  • Deterministic ECMWF-MOS (0000- and 1200-UTC cycles)
  • Ensemble ECMWF-MOS (0000- and 1200-UTC cycles)
  • EKDMOS (0600- and 1800-UTC cycles)
  • NAM-MOS (0000- and 1200-UTC cycles)
  • LAMP (hourly)

The blending process

The word blend in NBM represents the key feature of this model. A wide variety of models are blended into the NBM final result. This blending process has been shown to improve the skill level present in the individual models.

Cliff Mass, an Atmospheric Science professor at the University of Washington, has published a presentation describing the blending process, referred to as MOS. The presentation is an interesting read, if you want to understand some of the techniques that may be present in the NBM blending process.

The blend in the NBM is different from an ensemble model in two major ways.

  1. first, the blending in the NBM does not use simple averaging, and Prof. Mass’s paper talkes about how this may work, in some detail. The blending in NBM is much more sophisticated than a simple average, and it is able to improve the skill of the result as well as retain detail in the data.

  2. secondly, the input models to the blend are from a wide variety of sources, both from NOAA and from outside of NOAA. For example, models from both Canada and the US Navy are included as elements of the blend. Both high resolution regional models and global models are considered. In a way, the NBM is a meta-ensemble, an advanced blending of other ensemble (and non-ensemble) models

NBM Hawaii

Note that there are two NBM models which cover Hawaii. This one, provides data at a resolution of 2.5km. The other model is NBM Oceanic, which has a resolution of 10km.

NBM Oceanic contains probabilistic data, NBM Hawaii data is not probabilistic. This will be something to consider when you choose among these two models.

The ability of the Oceanic domain data to express degrees of certainty and uncertainty in its data is unique, and potentially very useful.

A short tutorial is available, describing the NBM Oceanic probabilistic data in some more detail.

Additional Information

For additional information, see:

Global Models

Large Regional

North America




OpenWRF - Europe

Server Status

Server Status for OpenWRF Models